NDLI: Human action segmentation with hierarchical supervoxel consistency

Content Provider	IEEE Xplore Digital Library
Author	Jiasen Lu Ran Xu Corso, J.J.
Copyright Year	2015
Description	Author affiliation: Comput. Sci. & Eng., SUNY at Buffalo, Buffalo, NY, USA (Jiasen Lu; Ran Xu) \|\| Electr. Eng. & Comput. Sci., Univ. of Michigan, Ann Arbor, MI, USA (Corso, J.J.)
Abstract	Detailed analysis of human action, such as action classification, detection and localization has received increasing attention from the community; datasets like JHMDB have made it plausible to conduct studies analyzing the impact that such deeper information has on the greater action understanding problem. However, detailed automatic segmentation of human action has comparatively been unexplored. In this paper, we take a step in that direction and propose a hierarchical MRF model to bridge low-level video fragments with high-level human motion and appearance; novel higher-order potentials connect different levels of the supervoxel hierarchy to enforce the consistency of the human segmentation by pulling from different segment-scales. Our single layer model significantly outperforms the current state-of-the-art on actionness, and our full model improves upon the single layer baselines in action segmentation.
Starting Page	3762
Ending Page	3771
File Size	3143434
Page Count	10
File Format	PDF
ISSN	10636919
e-ISBN	9781467369640
DOI	10.1109/CVPR.2015.7299000
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Motion segmentation Trajectory Cameras Shape Estimation Image edge detection Color
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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